2 citations · 2 across the 9 of their papers we have counts for
5 papers · 1 filter
Neural operators solve inverse problems for constitutive model discovery
Moritz Flaschel, Burigede Liu, Ellen Kuhl
Characterizing the mechanical response of materials traditionally requires solving optimization problems in which model parameters are calibrated or trained to minimize the discrep…
Adaptive Material Fingerprinting for the fast discovery of polyconvex feature combinations in isotropic and anisotropic hyperelasticity
Moritz Flaschel, Hagen Holthusen, Denisa Martonová +1
We recently proposed a method called Material Fingerprinting for the rapid discovery of mechanical material models that avoids solving continuous optimization problems. Material Fi…
Unsupervised Material Fingerprinting: Ultra-fast hyperelastic model discovery from full-field experimental measurements
Moritz Flaschel, Miguel Angel Moreno-Mateos, Simon Wiesheier +2
Material Fingerprinting is a lookup table-based strategy to discover material models from experimental measurements, which completely avoids the need to solve an optimization probl…
Material Fingerprinting: A shortcut to material model discovery without solving optimization problems
Moritz Flaschel, Denisa Martonová, Carina Veil +1
We propose Material Fingerprinting, a new method for the rapid discovery of mechanical material models from direct or indirect data that avoids solving potentially non-convex optim…
Non-smooth optimization meets automated material model discovery
Moritz Flaschel, Trevor Hastie, Ellen Kuhl
Automated material model discovery disrupts the tedious and time-consuming cycle of iteratively calibrating and modifying manually designed models. Non-smooth L1-norm regularizatio…